Large-area microscopy, sampling, super-resolution (SR) and image mosaicing has many applications. For example, demand for miniature and low-cost electronic devices, along with advances in materials, drives semiconductor and device manufacturing toward micro-scale and nano-scale patterns in large areas. Similarly, large-view and high precision imaging devices such as microscopes might be desirable for scientific and medical imaging. To inspect high-resolution patterns over a large range requires high-precision imaging technologies. For example, fast frame grabbers and optical microscopy techniques facilitate imaging at micrometer and nanometer scales. However, the field of view (FOV) of high-resolution microscopes fundamentally limits detailed pattern imaging over a large area.
Some current large-area microscopy solutions employ large FOV and high-resolution optical sensors, such as higher-powered optics and larger charge-coupled device (CCD) arrays. However, these sensors increase the cost of the imaging system. Other current large-area microscopy solutions implement lens-free large-area imaging systems with large FOV using computational on-chip imaging tools or miniaturized mirror optics. On-chip imaging employs digital optoelectronic sensor arrays to directly sample the light transmitted through a large-area specimen without using lenses between the specimen and sensor chip. Miniaturized mirror optics systems employ various mirror shapes and projective geometries to reflect light arrays from larger FOV into the smaller FOV of camera. However, both on-chip imaging and miniaturized mirror optics systems achieve limited spatial resolution. Moreover, on-chip imaging is limited to transmission microscopy modalities, and miniaturized mirror optics experience distortion and low contrast (e.g., due to variations or defects in mirror surfaces, etc.).
An alternative approach to large-area microscopy is to implement high-precision scanners at an effective scanning rate and stitch individual FOV images together into a wide view. During this process, fast scanners acquire multiple frames over a region of interest (ROI). Raster scanning is commonly employed for scanning small-scale features over large areas. In raster scanning, samples are scanned back and forth in one Cartesian coordinate, and shifted in discrete steps in another Cartesian coordinate. Fast and accurate scanning requires precise positioning with low vibration and short settling times. However, fast positioning relies on high velocities and high accelerations that often induce mechanical vibrations. Techniques for reducing vibration in a raster scan tend to increase the size and cost of mechanical structures (e.g., requiring larger and more robust mechanical supports, etc.), or can be complex and/or sensitive to measurement noise during a scan (e.g., complex control systems, etc.).
Another approach to reducing mechanical vibrations is to employ smooth scanning trajectories that limit jerk and acceleration without additional large mechanical structures or complex control techniques. Such trajectories include spiral, cycloid, and Lissajous scan patterns, which allow high imaging speeds without exciting resonances of scanners and without complex control techniques. However, such scan trajectories do not achieve uniform sample point spatial distribution in Cartesian coordinates, resulting in distortion errors in sampled images.
Thus, there is a need for improved large-area microscopy, sampling, super-resolution (SR) and image mosaicing systems and techniques.